Gopal S Akshintala

5 things that quietly break when you migrate AEM to Cloud Service: 1. Custom run modes — Cloud only supports dev, stage, and prod. If you built environment-specific logic around custom run modes, it all needs refactoring. 2. Mutable vs immutable content — Anything under /apps and /libs can't be modified at runtime. Components that write back to the repository? They won't work. 3. Dispatcher configurations — Cloud Service Dispatcher is fundamentally different. Your carefully tuned cache rules need a complete rethink. 4. Scheduled jobs and workflows — Sling scheduler jobs behave differently. Long-running workflows need to be redesigned for cloud architecture. 5. Direct repository access — No more CRXDE in production. No more JMX console. Your debugging approach changes entirely. None of these show up as errors on day one. They surface weeks later, when your team is publishing at full speed. The fix isn't working harder. It's assessing earlier. What's been your biggest AEM Cloud migration surprise? Drop it in the comments. #AEM #AdobeExperienceManager #CloudMigration #ContentManagement #EnterpriseTech

23

2 Comments

"A lot of engineers think that code quality is important to building a successful product. The two have nothing to do with each other." Block CTO Dhanji R. Prasanna

223

35 Comments

𝐄𝐧𝐡𝐚𝐧𝐜𝐢𝐧𝐠 𝐀𝐄𝐌 𝐏𝐚𝐠𝐞 𝐏𝐫𝐨𝐩𝐞𝐫𝐭𝐢𝐞𝐬: 𝐀 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫'𝐬 𝐆𝐮𝐢𝐝𝐞 In AEM, Page Properties serve as crucial metadata that define and manage webpage content. While AEM provides a standard set of properties, there are scenarios where extending these properties becomes essential to meet specific business requirements. Here’s a practical breakdown of 3 ways to extend AEM page properties: ➕ 𝐀𝐝𝐝 𝐚 𝐜𝐮𝐬𝐭𝐨𝐦 𝐭𝐚𝐛 – for things like “Content Approval” or custom metadata. 🧩 𝐄𝐱𝐭𝐞𝐧𝐝 𝐞𝐱𝐢𝐬𝐭𝐢𝐧𝐠 𝐭𝐚𝐛𝐬 – insert new fields where they naturally belong. 🧼 𝐂𝐥𝐞𝐚𝐧 𝐮𝐩 𝐭𝐡𝐞 𝐔𝐈 – hide tabs or fields that authors don’t need to see. For a comprehensive walkthrough and code examples, check out the full article here: https://lnkd.in/gix6kia7 #AEM #DigitalExperience #AEMTips #ContentManagement #BrainStation23

19

Cluelabs AI storyboarding helps e‑learning teams transform a course idea into a complete storyboard automatically—outlines, slides, quizzes and interactions—freeing developers to focus on creative refinements. The result is consistent, high‑quality courses produced faster and at scale.

🚨 𝗦𝘁𝗼𝗽 𝗖𝗮𝗹𝗹𝗶𝗻𝗴 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗮 “𝗟𝗮𝘆𝗲𝗿” 𝗶𝗻 𝗔𝗜 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 One mistake I keep seeing in 𝗚𝗲𝗻𝗔𝗜 / 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 architectures 👇 People draw diagrams with 10–15 layers. 🧱 𝗠𝗲𝗺𝗼𝗿𝘆 𝗟𝗮𝘆𝗲𝗿 🧱 𝗧𝗼𝗼𝗹 𝗟𝗮𝘆𝗲𝗿 🧱 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 🧱 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 🧱 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 But here’s the problem. 𝗡𝗼𝘁 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗶𝘀 𝗮 𝗹𝗮𝘆𝗲𝗿. And this creates confusion for 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘀 and 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀. 💡 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗮 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 When we call something a “𝗹𝗮𝘆𝗲𝗿”, developers immediately think: • There must be 𝗰𝗼𝗱𝗲 here • There must be 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀 / 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 • There must be 𝗺𝗼𝗱𝘂𝗹𝗲𝘀 to implement But sometimes what we are actually describing is just a 𝗳𝗹𝗼𝘄 𝗼𝗿 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆, not a layer. That leads to confusion like: 👉 “𝗪𝗵𝗮𝘁 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝗱𝗼 𝘄𝗲 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗵𝗲𝗿𝗲?” ⚙️ 𝗧𝗵𝗶𝗻𝗸 𝗶𝗻 𝗧𝗲𝗿𝗺𝘀 𝗼𝗳 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝗜𝗻𝘀𝘁𝗲𝗮𝗱 Instead of forcing everything into layers, describe 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 or 𝗳𝗹𝗼𝘄𝘀. Example in 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 systems: 🔹 𝗔𝗴𝗲𝗻𝘁 𝗥𝘂𝗻𝘁𝗶𝗺𝗲 Where agents execute (Bedrock AgentCore / custom runtime) 🔹 𝗠𝗲𝗺𝗼𝗿𝘆 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Short-term + long-term memory (Redis, PG, Vector DB) 🔹 𝗧𝗼𝗼𝗹𝗶𝗻𝗴 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 APIs, MCP tools, enterprise services 🔹 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗙𝗹𝗼𝘄 How agents collaborate (Supervisor, Planner-Executor-Reviewer) Notice something? These are 𝗻𝗼𝘁 𝘀𝘁𝗮𝗰𝗸𝗲𝗱 𝗹𝗮𝘆𝗲𝗿𝘀. They are 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 interacting with each other. 🧠 𝗚𝗼𝗼𝗱 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝘀𝗵𝗼𝘂𝗹𝗱 𝗮𝗻𝘀𝘄𝗲𝗿 𝗼𝗻𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 👉 “𝗪𝗵𝗮𝘁 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝗯𝗲 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱?” If developers cannot answer that after seeing the diagram, the architecture is still unclear. 🔥 𝗠𝗼𝗱𝗲𝗿𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗶𝘀 𝗺𝗼𝗿𝗲 𝗮 𝗙𝗟𝗢𝗪 𝘁𝗵𝗮𝗻 𝗮 𝗟𝗔𝗬𝗘𝗥 𝗖𝗔𝗞𝗘 Agents ↓ Tools ↓ Memory ↓ Orchestration ↓ Observability Everything 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝘀 𝗱𝘆𝗻𝗮𝗺𝗶𝗰𝗮𝗹𝗹𝘆. 💬 Curious to hear from others building 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 systems: Do you prefer designing architectures as: 1️⃣ 𝗟𝗮𝘆𝗲𝗿𝗲𝗱 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 2️⃣ 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆-𝗯𝗮𝘀𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 3️⃣ 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗳𝗹𝗼𝘄𝘀

7

2 Comments

𝐓𝐡𝐞 𝐬𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭 𝐭𝐡𝐚𝐭 𝐜𝐨𝐝𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐀𝐈 𝐢𝐬 𝐥𝐢𝐤𝐞 𝐩𝐚𝐢𝐫𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐚 𝐣𝐮𝐧𝐢𝐨𝐫 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐧𝐨 𝐥𝐨𝐧𝐠𝐞𝐫 𝐟𝐞𝐞𝐥𝐬 𝐭𝐫𝐮𝐞. It's no longer “reviewing every single line of code.” It’s shifted to design, intent, trade‑offs—and having the mechanical translation of those decisions handled instantly. And honestly? That translation was always the boring part. Working with AI now feels like pair programming with a hyper‑capable colleague who never gets tired and knows every API by heart. I’m still making the decisions. I’m still accountable for the design. But the execution happens at conversation speed instead of typing speed. The craft isn’t disappearing. The friction is.

35

4 Comments

🚀 Here’s how people are jumping from 5 LPA → 20 LPA: 1. Learning hot skills that ride on the AI wave: • Prompt Engineering for enterprise AI tools • Data Engineering with LangChain, Vector DBs (like Pinecone, Chroma) • AI-native Frontend: integrating LLMs with React/Next.js • Backend APIs for LLM orchestration (FastAPI, Flask, Node) • AI Ops: Model monitoring, fine-tuning, and performance tracking • GenAI product thinking: Building tools with AI at the core 2. Positioning themselves smartly on LinkedIn & GitHub Recruiters don’t hire the best—they hire the most visible. 3. Using AI to beat the job application funnel From résumé tailoring to mock interviews to real-time coding help—AI isn’t just helping people work, it’s helping them get hired. ⸻ 💡 The best part? You don’t need to become a Data Scientist. You need to become “AI-literate” in your own field. Marketing? Start using AI for campaign automation. Sales? Learn AI-based CRM workflows. HR? Use AI for sourcing, screening, JD optimization. Developer? Learn to build with LLMs. ⸻ 💬 Curious? Drop a 🔥 if you want me to share a practical step-by-step roadmap. Let’s normalize smart career growth—not burnout. #AI #CareerGrowth #LinkedInTips #JobSwitch #ArtificialIntelligence #PromptEngineering #LLMs #GenAI #SalaryGrowth

5

2 Comments

Reusable coding solutions, explained like never before. Design Patterns = smarter ways to solve recurring problems in software development. Kejriwal & Rahul Ji bring humor + clarity together 🎯 Watch, learn, and share with your dev teams! #SoftwareEngineering #DesignPatterns #GrowXenExplains

1

𝐓𝐨𝐩 𝟓 𝐋𝐋𝐌 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬 𝐓𝐡𝐚𝐭 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐌𝐚𝐭𝐭𝐞𝐫 𝐢𝐧 𝟐𝟎𝟐𝟔 #LLMs are no longer judged by size alone. In #2026, what separates demos from #production systems is #architecture. Here are the 5 #LLM #architectures every serious #AIEngineer should understand: 𝟏 #𝐁𝐚𝐬𝐞 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬 • Pretrained on massive corpora • General reasoning and language capability • No domain awareness by default Powerful, but blind without structure. 𝟐 #𝐑𝐀𝐆 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 • External knowledge via #retrieval • #Vector search, reranking, grounding • Reduces #hallucinations #RAG turns static #models into context-aware systems. 𝟑 #𝐓𝐨𝐨𝐥 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐋𝐋𝐌𝐬 • Function calling and #API execution • #Databases, #workflows, #services • From text to real actions #LLMs stop talking and start doing. 𝟒 #𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐋𝐋𝐌 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 • #Planning, #memory, feedback loops • Multi step reasoning • Goal driven execution This is where LLMs become decision engines. 𝟓 #𝐕𝐞𝐫𝐢𝐟𝐢𝐞𝐝 & #𝐆𝐨𝐯𝐞𝐫𝐧𝐞𝐝 𝐋𝐋𝐌 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 • Validation layers • Guardrails, policies, constraints • Observability and auditability If it can’t be verified, it doesn’t belong in #EnterpriseAI. Architectures decide reliability, scale, and trust. LLMs are components. Systems are the product. So, build architectures. Not prompts. #LLMArchitecture #GenAI #AgenticAI #RAG #AIArchitecture #LLMSystems #ProductionAI #AIEngineering #FutureOfAI

4

NgRx Signal Store with RxMethods in Angular 19 Angular 19 revolutionizes state management by combining NgRx Signal Store with RxMethods. This approach eliminates traditional boilerplate while maintaining robustness. Developers now get intuitive state management with improved performance, better type safety, and seamless reactive programming integration—perfect for projects of any size. Check out the below article

1

Promise vs Observable vs Subscription in Angular with a timeline of execution #Promise - Single Value A Promise resolves only once — it either succeeds with a value or fails with an error. After that, it cannot emit another value. #Observable - Multiple Values An Observable can emit zero, one, or many values over time. Each time it emits a value, subscribers receive it. It can continue emitting until it completes or is unsubscribed. #Subscription A Subscription is the object you get when you subscribe to an Observable. It represents the execution of that Observable. You can unsubscribe to stop receiving values and free resources. #RuleOfThumb: Prefer Observable + async pipe in Angular templates. Use Promise + async/await in services or one-off calls. Keep Subscriptions only when you need manual control and clean-up.

17

One of the most common misconceptions I hear about AEM Content Fragments and Adobe Journey Optimizer (AJO) is this: “AEM exports Content Fragments into the AJO Offer Library.” That’s not actually how it works. 👉 This is not literally “export to AJO Offer Library” like the Target export workflow 👉 It is AJO consuming AEM Content Fragments as external content And that distinction matters. In AEM as a Cloud Service, Content Fragments act as a content source of truth, while AJO Offer Library acts as the decisioning container. The relationship looks more like this: AEM → content management + lifecycle AJO Offer Library → decisioning + eligibility + scheduling Journey/Campaign → orchestration layer Instead of copying content into AJO, AJO references Content Fragments directly from AEM using Adobe’s cloud content integration. This creates a clean separation of responsibilities: • Authors manage content in AEM • Marketers manage offers and eligibility in AJO • Decisioning selects what to show • Channels render the experience --- How lifecycle works in practice Content Fragment lifecycle is governed in AEM: • Authoring and approvals • Versioning • Publishing to AEM delivery tier • Content updates immediately reflected when referenced Offer lifecycle is governed in AJO: • Offer start/end dates • Eligibility rules • Ranking and decision strategies • Placement configuration This means: Updating a published Content Fragment in AEM does not require republishing the offer in AJO — AJO simply resolves the latest fragment content at runtime. The Offer Library controls when and to whom content is eligible, while AEM controls what the content actually is. Think of it as: “AJO schedules and decides. AEM supplies the content.” --- Why this model is powerful This architecture prevents content duplication across systems and allows: • Centralized content governance in AEM • Faster campaign updates without rebuilding offers • Reuse of fragments across journeys and channels • Cleaner separation between content and decisioning It also reflects Adobe’s broader Experience Cloud direction: AEM = content supply chain AJO = orchestration + decisioning --- For teams moving to AEM as a Cloud Service + Journey Optimizer, this pattern is worth understanding early. It simplifies operations and aligns authoring, marketing, and personalization workflows. Curious how others are structuring their AEM → AJO content reuse patterns. #AdobeExperienceManager #AEM #AdobeJourneyOptimizer #AJO #AdobeExperienceCloud

63

8 Comments

There’s a chaatwala in Delhi who serves incredible volumes during lunch hour. All ingredients are pre‑chopped and arranged in sequence, multiple orders processed assembly‑line style, and different components prepared in parallel. Every movement is optimized to minimize wasted time and effort. It hit me right then. That's 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 in their purest form. The chaatwala has intuitively discovered principles formalized as optimization algorithms, minimizing latency and maximizing throughput. 𝐇𝐨𝐰 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐰𝐨𝐫𝐤𝐬 𝐢𝐧 𝐭𝐞𝐜𝐡: 𝟏. 𝐂𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐏𝐚𝐭𝐡 𝐌𝐞𝐭𝐡𝐨𝐝 (𝐂𝐏𝐌) He identifies which steps can run in parallel and which must be sequential, like project management tools calculate critical paths. Build systems like Make use dependency graphs to determine optimal execution order. 𝟐. 𝐊𝐚𝐧𝐛𝐚𝐧 𝐒𝐲𝐬𝐭𝐞𝐦 His limited counter space functions as a Kanban board, preventing work‑in‑progress accumulation and ensuring smooth flow. 𝟑. 𝐓𝐡𝐞𝐨𝐫𝐲 𝐨𝐟 𝐂𝐨𝐧𝐬𝐭𝐫𝐚𝐢𝐧𝐭𝐬 He optimizes the slowest step (e.g., cooking time), similar to how engineers optimize bottleneck resources. 𝟒. 𝐋𝐞𝐚𝐧 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 He eliminates waste, streamlines flow, and continuously improves, mirroring lean manufacturing and CI/CD workflow optimizations. 𝐑𝐞𝐚𝐥‑𝐰𝐨𝐫𝐥𝐝 𝐢𝐦𝐩𝐚𝐜𝐭: Modern CI/CD pipelines, data processing systems, and assembly‑line operations use similar workflow optimization techniques to execute tasks efficiently, balancing parallel and sequential steps. The chaatwala demonstrates that effective optimizations often come from deep domain knowledge and continuous incremental improvements rather than theory alone.

1

🤯 Just shipped a full-stack note-taking app — and Claude wrote the entire spec. Meet NoteShare 📝 A rich-text note-taking web app built for the way people actually work: ✍️ TipTap editor — bold, italic, headings, code blocks, bullet lists. Auto-saves as you type, no manual save button needed. 🔐 Auth done right — email + password, Google SSO, GitHub SSO. All handled by better-auth in one config file. 🔗 Public sharing — toggle any note public, get a unique URL, and send it directly to anyone via email or SMS. ⏰ Smart timestamps — every date shows in your local timezone. Recent notes show "2 hours ago". Older ones show the full date. 🚀 Deployed on Fly.io free tier — SQLite on a persistent volume. One fly deploy, and it's live. No database service. No credit card. Here's the wild part. The entire technical spec — data model, SQL schema, API design, component breakdown, security considerations, deployment guide — was built in a conversation with Claude. What used to take days of architecture meetings and back-and-forth with senior engineers? A few hours. One chat window. Claude didn't just write code. It thought through edge cases, rate limiting, cross-user security, slug entropy, and auto-save debounce logic. Things you'd normally miss on the first pass. If you're a developer still doing all of this from scratch on your own, the game has changed. Start your Claude Code journey today. Seriously. Now. 🤖⚡ 🌐 Live app: https://noteshare.fly.dev ⭐ GitHub: https://lnkd.in/g6Jn7Ny6 #ClaudeCode #AI #BuildInPublic #Developer #NextJS #Bun #TypeScript #IndieDev #ShipIt #OpenSource

14

Vibe coding gets press—both good and bad. So what's the truth? The truth IMHO is that it has its place.. it will work for a greenfield project, a prototype an isolated feature etc. Though do not try it on an old dense forest of code where sunlight does not reach the floor, vines entangle anyone who ventures etc. but more about that later. Back to vibe coding itself. Some people try vibe for exactly two days ( that's how long a weekend is) and give up because it doesn’t deliver the magical, instant results they were promised. Then there are those who vibe code for a week, only to discover their shiny new app has turned into an unmaintainable spaghetti monster. These are programmers who actually understand what “maintainability” means—so they bail before it eats their weekends. Next, we have the folks who vibe code for a week and hit a wall: the agent can’t fix an important bug, and they have absolutely no clue what to do next. These are people with little or no programming experience—or at least no knowledge of the tech they’re vibe coding with—so they, too, give up. But then… there’s another kind. The kind who mix their programming skills with deliberate, methodical planning and persistence to make vibe coding work. This kind learns to use it effectively. They master prompting (I refuse to call it “prompt engineering”), context engineering (okay, maybe there’s some engineering here), and writing PRDs—Product Requirements Documents—for the uninitiated. They learn to write instructions for agent in co-pilot, use hierarchical rules in Cursor, hand craft CLAUDE.md in Claude Code. They realise coding agents are like wild horses: they need to be tamed. You have to break them in, fit the harness, and put on blinders so they don’t wander off into a field of half-baked features. Once tamed, the horse becomes your friend. Similarly, once you learn to wrangle the coding agent, it becomes your partner in crime (minus the prison sentence). But this isn’t a one-time effort. You have to keep feeding it the right context, plus detailed prompts packed with your entire brain dump for a feature—only then can you expect it to behave. It stops feeling like magic and starts feeling more like hard labour. Like Abraham Lincoln said: spend three hours sharpening the axe (planning and giving context) for every hour cutting down the tree (coding). So, if you’re starting down this path, be warned: vibe coding promises faster development, but the toil doesn’t disappear. You’re just moving it—from coding to elaborate planning.

108

9 Comments

🚀✨ Q3 wrapped up with powerful updates for compliance, faster automation, and accelerated recurring workflows. #Signeasy #eSignature #FDACompliance #21CFRPart11 #ContractManagement #HRTech #Teamtailor #AppleIntelligence #DigitalSignatures

6

💻 𝗛𝗼𝘄 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗲𝗿𝘀 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗦𝗹𝗲𝗲𝗽 😴🐍 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞 :-https://lnkd.in/gvFjBf2z Every programmer knows this struggle — you go to bed thinking about bugs, and suddenly at 3 AM, the perfect fix hits you! 😂 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 🔟 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘁𝗶𝗽𝘀 𝘁𝗼 𝗵𝗲𝗹𝗽 𝗣𝘆𝘁𝗵𝗼𝗻 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 (𝗺𝗮𝘆𝗯𝗲) 𝘀𝗹𝗲𝗲𝗽 𝗯𝗲𝘁𝘁𝗲𝗿 🧠💤👇 𝟏️⃣ 𝐏𝐥𝐚𝐧 𝐁𝐞𝐟𝐨𝐫𝐞 𝐘𝐨𝐮 𝐂𝐨𝐝𝐞 — Avoid late-night debugging by outlining your logic clearly. 𝟐️⃣ 𝐔𝐬𝐞 𝐕𝐢𝐫𝐭𝐮𝐚𝐥 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬 — Keep dependencies clean; reduce those mysterious “import errors.” 𝟑️⃣ 𝐂𝐨𝐦𝐦𝐞𝐧𝐭 𝐘𝐨𝐮𝐫 𝐂𝐨𝐝𝐞 — Future you will thank you for not staying up decoding your own logic! 𝟒️⃣ 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐑𝐞𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 𝐓𝐚𝐬𝐤𝐬 — Let Python do the boring stuff while you dream of efficiency. 𝟓️⃣ 𝐔𝐬𝐞 𝐃𝐞𝐛𝐮𝐠𝐠𝐞𝐫𝐬 — Master tools like pdb or VS Code debugger to find bugs faster. 𝟔️⃣ 𝐅𝐨𝐥𝐥𝐨𝐰 𝐏𝐄𝐏 𝟖 — Clean, consistent code means fewer mental bugs to haunt your sleep. 𝟕️⃣ 𝐖𝐫𝐢𝐭𝐞 𝐔𝐧𝐢𝐭 𝐓𝐞𝐬𝐭𝐬 — Testing early saves countless 3 AM “what broke now?” moments. 𝟖️⃣ 𝐑𝐞𝐚𝐝 𝐓𝐫𝐚𝐜𝐞𝐛𝐚𝐜𝐤𝐬 𝐂𝐚𝐫𝐞𝐟𝐮𝐥𝐥𝐲 — They usually tell you exactly what went wrong (and where). 𝟗️⃣ 𝐔𝐬𝐞 𝐕𝐞𝐫𝐬𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 — Git isn’t just backup; it’s your time machine when things go south. 🔟 𝐓𝐚𝐤𝐞 𝐁𝐫𝐞𝐚𝐤𝐬 𝐚𝐧𝐝 𝐒𝐥𝐞𝐞𝐩 𝐖𝐞𝐥𝐥 — Even the best code looks better after rest and coffee . Python developers — how many nights have you spent “debugging in dreams”? 😂👇

91

At #apidaysIndia, Naresh Jain will unveil a groundbreaking approach to API workflow testing that bridges REST and event-driven architectures. His session "From REST to Events: API Workflow Testing and Mocking with a Single Arazzo Spec" promises to revolutionize how we think about API testing and mocking. Using the Arazzo Specification, Naresh will demonstrate how to drive end-to-end API workflow testing across synchronous and asynchronous operations. Want to see how mocking complete workflows can unblock development and accelerate CI pipelines? Read more about his apidays India session here: https://lnkd.in/gaRWWMPk #apidays #Specmatic #APIdesign #APIWorkflowTesting #Arazzo #APImocking #APItesting Specmatic

2